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提交 1f833c24 authored 作者: Frederic's avatar Frederic
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theano/tensor/tests/test_basic.py
浏览文件 @ 1f833c24
......@@ -1449,6 +1449,7 @@ class T_Shape(unittest.TestCase):
s = shape(numpy.ones((5, 3, 10)))
self.assertTrue((eval_outputs([s]) == [5, 3, 10]).all())
class T_max_and_argmax(unittest.TestCase):
def setUp(self):
utt.seed_rng()
......@@ -1456,108 +1457,110 @@ class T_max_and_argmax(unittest.TestCase):
def test0(self):
n = as_tensor_variable(5.0)
v,i = eval_outputs(max_and_argmax(n))
v, i = eval_outputs(max_and_argmax(n))
self.assertTrue(v == 5.0)
self.assertTrue(i == 0)
v = eval_outputs(max_and_argmax(n)[0].shape)
assert len(v)==0
assert len(v) == 0
v = eval_outputs(max_and_argmax(n)[1].shape)
assert len(v)==0
assert len(v) == 0
def test1(self):
n = as_tensor_variable([1,2,3,2,-6])
v,i = eval_outputs(max_and_argmax(n))
n = as_tensor_variable([1, 2, 3, 2, -6])
v, i = eval_outputs(max_and_argmax(n))
self.assertTrue(v == 3)
self.assertTrue(i == 2)
v = eval_outputs(max_and_argmax(n)[0].shape)
assert len(v)==0
assert len(v) == 0
def test2(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
v,i = eval_outputs(max_and_argmax(n,-1))
self.assertTrue(numpy.all(v == numpy.max(data,-1)))
self.assertTrue(numpy.all(i == numpy.argmax(data,-1)))
v = eval_outputs(max_and_argmax(n,-1)[0].shape)
assert v==(2)
v, i = eval_outputs(max_and_argmax(n, -1))
self.assertTrue(numpy.all(v == numpy.max(data, -1)))
self.assertTrue(numpy.all(i == numpy.argmax(data, -1)))
v = eval_outputs(max_and_argmax(n, -1)[0].shape)
assert v == (2)
def test2b(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
v,i = eval_outputs(max_and_argmax(n,0))
self.assertTrue(numpy.all(v == numpy.max(data,0)))
self.assertTrue(numpy.all(i == numpy.argmax(data,0)))
v = eval_outputs(max_and_argmax(n,0)[0].shape)
assert v==(3)
v = eval_outputs(max_and_argmax(n,1)[0].shape)
assert v==(2)
v, i = eval_outputs(max_and_argmax(n, 0))
self.assertTrue(numpy.all(v == numpy.max(data, 0)))
self.assertTrue(numpy.all(i == numpy.argmax(data, 0)))
v = eval_outputs(max_and_argmax(n, 0)[0].shape)
assert v == (3)
v = eval_outputs(max_and_argmax(n, 1)[0].shape)
assert v == (2)
#currently not supported
#v = eval_outputs(max_and_argmax(n,[0,1])[0].shape)
#assert v.size==0
def test2_invalid(self):
n = as_tensor_variable(numpy.random.rand(2,3))
n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL)
try:
try:
eval_outputs(max_and_argmax(n,3))
eval_outputs(max_and_argmax(n, 3))
assert False
except ValueError, e:
pass
finally:
_logger.setLevel(oldlevel)
def test2_invalid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3))
n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
try:
eval_outputs(max_and_argmax(n,-3))
eval_outputs(max_and_argmax(n, -3))
assert False
except ValueError, e:
pass
finally:
sys.stderr = old_stderr
def test2_valid_neg(self):
n = as_tensor_variable(numpy.random.rand(2,3))
v,i = eval_outputs(max_and_argmax(n,-1))
n = as_tensor_variable(numpy.random.rand(2, 3))
v, i = eval_outputs(max_and_argmax(n, -1))
self.assertTrue(v.shape == (2,))
self.assertTrue(i.shape == (2,))
self.assertTrue(numpy.all(v == numpy.max(n.value,-1)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value,-1)))
v,i = eval_outputs(max_and_argmax(n,-2))
self.assertTrue(numpy.all(v == numpy.max(n.value, -1)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value, -1)))
v, i = eval_outputs(max_and_argmax(n, -2))
self.assertTrue(v.shape == (3,))
self.assertTrue(i.shape == (3,))
self.assertTrue(numpy.all(v == numpy.max(n.value,-2)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value,-2)))
v = eval_outputs(max_and_argmax(n,-1)[0].shape)
assert v==(2)
v = eval_outputs(max_and_argmax(n,-2)[0].shape)
assert v==(3)
self.assertTrue(numpy.all(v == numpy.max(n.value, -2)))
self.assertTrue(numpy.all(i == numpy.argmax(n.value, -2)))
v = eval_outputs(max_and_argmax(n, -1)[0].shape)
assert v == (2)
v = eval_outputs(max_and_argmax(n, -2)[0].shape)
assert v == (3)
def test3(self):
n = as_tensor_variable(numpy.random.rand(2,3,4))
v,i = eval_outputs(max_and_argmax(n,0))
self.assertTrue(v.shape == (3,4))
self.assertTrue(i.shape == (3,4))
v,i = eval_outputs(max_and_argmax(n,1))
self.assertTrue(v.shape == (2,4))
self.assertTrue(i.shape == (2,4))
v,i = eval_outputs(max_and_argmax(n,2))
self.assertTrue(v.shape == (2,3))
self.assertTrue(i.shape == (2,3))
v = eval_outputs(max_and_argmax(n,0)[0].shape)
assert tuple(v)==(3,4)
v = eval_outputs(max_and_argmax(n,1)[0].shape)
assert tuple(v)==(2,4)
v = eval_outputs(max_and_argmax(n,2)[0].shape)
assert tuple(v)==(2,3)
n = as_tensor_variable(numpy.random.rand(2, 3, 4))
v, i = eval_outputs(max_and_argmax(n, 0))
self.assertTrue(v.shape == (3, 4))
self.assertTrue(i.shape == (3, 4))
v, i = eval_outputs(max_and_argmax(n, 1))
self.assertTrue(v.shape == (2, 4))
self.assertTrue(i.shape == (2, 4))
v, i = eval_outputs(max_and_argmax(n, 2))
self.assertTrue(v.shape == (2, 3))
self.assertTrue(i.shape == (2, 3))
v = eval_outputs(max_and_argmax(n, 0)[0].shape)
assert tuple(v) == (3, 4)
v = eval_outputs(max_and_argmax(n, 1)[0].shape)
assert tuple(v) == (2, 4)
v = eval_outputs(max_and_argmax(n, 2)[0].shape)
assert tuple(v) == (2, 3)
def test_grad(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
def check_grad_max(data, max_grad_data, axis=None):
......@@ -1565,35 +1568,39 @@ class T_max_and_argmax(unittest.TestCase):
Why this is needed? verify_grad is not enought?
"""
#This work only for axis in [0,None]
assert axis in [0,None]
assert axis in [0, None]
z = numpy.zeros_like(data)
z = z.flatten()
argmax=numpy.argmax(data,axis=axis)
if argmax.ndim==0:
z[argmax]+=1
argmax = numpy.argmax(data, axis=axis)
if argmax.ndim == 0:
z[argmax] += 1
else:
for id,v in enumerate(argmax):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
for id, v in enumerate(argmax):
z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape)
assert numpy.all(max_grad_data == z)
#test grad of max
#axis is the last one
utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[1], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=-1)[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=-1)[1], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[1], [data])
check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=0)[0].sum(),n)),axis=0)
utt.verify_grad(lambda v: max_and_argmax(v, axis=[0])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=[0])[1], [data])
check_grad_max(data, eval_outputs(grad(
max_and_argmax(n, axis=0)[0].sum(), n)), axis=0)
utt.verify_grad(lambda v: max_and_argmax(v,axis=[1])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v,axis=[1])[1], [data])
#check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=1)[0],n)),axis=1)
utt.verify_grad(lambda v: max_and_argmax(v, axis=[1])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=[1])[1], [data])
#check_grad_max(data,eval_outputs(grad(
# max_and_argmax(n,axis=1)[0],n)),axis=1)
utt.verify_grad(lambda v: max_and_argmax(v.flatten())[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v.flatten())[1], [data])
check_grad_max(data,eval_outputs(grad(max_and_argmax(n.flatten())[0],n)))
check_grad_max(data, eval_outputs(grad(
max_and_argmax(n.flatten())[0], n)))
# Test 4d inner dimensions
data = numpy.random.rand(2, 3, 4, 5)
......@@ -1608,60 +1615,60 @@ class T_argmin_argmax(unittest.TestCase):
utt.seed_rng()
MaxAndArgmax.debug = 0
def test0(self):
for fct in [argmin,argmax]:
def test_scalar(self):
for fct in [argmin, argmax]:
n = as_tensor_variable(5.0)
i = eval_outputs(fct(n))
self.assertTrue(i == 0)
v = eval_outputs(fct(n).shape)
assert len(v)==0
assert len(v) == 0
def test1(self):
n = as_tensor_variable([1,2,3,2,-6])
def test_list(self):
n = as_tensor_variable([1, 2, 3, 2, -6])
i = eval_outputs(argmin(n))
self.assertTrue(i == 4)
v = eval_outputs(argmin(n).shape)
assert len(v)==0
assert len(v) == 0
n = as_tensor_variable([1,2,3,2,-6])
n = as_tensor_variable([1, 2, 3, 2, -6])
i = eval_outputs(argmax(n))
self.assertTrue(i == 2)
v = eval_outputs(argmax(n).shape)
assert len(v)==0
assert len(v) == 0
def test2(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
data = numpy.random.rand(2,3)
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
i = eval_outputs(fct(n,-1))
self.assertTrue(numpy.all(i == nfct(data,-1)))
v = eval_outputs(fct(n,-1).shape)
assert v==(2)
i = eval_outputs(fct(n, -1))
self.assertTrue(numpy.all(i == nfct(data, -1)))
v = eval_outputs(fct(n, -1).shape)
assert v == (2)
def test2b(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
data = numpy.random.rand(2,3)
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
i = eval_outputs(fct(n,0))
self.assertTrue(numpy.all(i == nfct(data,0)))
v = eval_outputs(fct(n,0).shape)
assert v==(3)
v = eval_outputs(fct(n,1).shape)
assert v==(2)
i = eval_outputs(fct(n, 0))
self.assertTrue(numpy.all(i == nfct(data, 0)))
v = eval_outputs(fct(n, 0).shape)
assert v == (3)
v = eval_outputs(fct(n, 1).shape)
assert v == (2)
#currently not supported
#v = eval_outputs(fct(n,[0,1]).shape)
#assert v.size==0
def test2_invalid(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3))
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL)
try:
try:
eval_outputs(fct(n,3))
eval_outputs(fct(n, 3))
assert False
except ValueError, e:
pass
......@@ -1669,13 +1676,13 @@ class T_argmin_argmax(unittest.TestCase):
_logger.setLevel(oldlevel)
def test2_invalid_neg(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3))
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
try:
eval_outputs(fct(n,-3))
eval_outputs(fct(n, -3))
assert False
except ValueError, e:
pass
......@@ -1683,286 +1690,297 @@ class T_argmin_argmax(unittest.TestCase):
sys.stderr = old_stderr
def test2_valid_neg(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3))
i = eval_outputs(fct(n,-1))
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
i = eval_outputs(fct(n, -1))
self.assertTrue(i.shape == (2,))
self.assertTrue(numpy.all(i == nfct(n.value,-1)))
i = eval_outputs(fct(n,-2))
self.assertTrue(numpy.all(i == nfct(n.value, -1)))
i = eval_outputs(fct(n, -2))
self.assertTrue(i.shape == (3,))
self.assertTrue(numpy.all(i == nfct(n.value,-2)))
self.assertTrue(numpy.all(i == nfct(n.value, -2)))
v = eval_outputs(fct(n,-1).shape)
assert v==(2)
v = eval_outputs(fct(n,-2).shape)
assert v==(3)
v = eval_outputs(fct(n, -1).shape)
assert v == (2)
v = eval_outputs(fct(n, -2).shape)
assert v == (3)
def test3(self):
for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2,3,4))
i = eval_outputs(fct(n,0))
self.assertTrue(i.shape == (3,4))
self.assertTrue(numpy.all(i == nfct(n.value,0)))
i = eval_outputs(fct(n,1))
self.assertTrue(i.shape == (2,4))
self.assertTrue(numpy.all(i == nfct(n.value,1)))
i = eval_outputs(fct(n,2))
self.assertTrue(i.shape == (2,3))
self.assertTrue(numpy.all(i == nfct(n.value,2)))
v = eval_outputs(fct(n,0).shape)
assert tuple(v)==(3,4)
v = eval_outputs(fct(n,1).shape)
assert tuple(v)==(2,4)
v = eval_outputs(fct(n,2).shape)
assert tuple(v)==(2,3)
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3, 4))
i = eval_outputs(fct(n, 0))
self.assertTrue(i.shape == (3, 4))
self.assertTrue(numpy.all(i == nfct(n.value, 0)))
i = eval_outputs(fct(n, 1))
self.assertTrue(i.shape == (2, 4))
self.assertTrue(numpy.all(i == nfct(n.value, 1)))
i = eval_outputs(fct(n, 2))
self.assertTrue(i.shape == (2, 3))
self.assertTrue(numpy.all(i == nfct(n.value, 2)))
v = eval_outputs(fct(n, 0).shape)
assert tuple(v) == (3, 4)
v = eval_outputs(fct(n, 1).shape)
assert tuple(v) == (2, 4)
v = eval_outputs(fct(n, 2).shape)
assert tuple(v) == (2, 3)
def test_grad_argmin(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
#test grad of argmin
utt.verify_grad(lambda v: argmin(v,axis=-1), [data])
utt.verify_grad(lambda v: argmin(v, axis=-1), [data])
utt.verify_grad(lambda v: argmin(v,axis=[0]), [data])
utt.verify_grad(lambda v: argmin(v, axis=[0]), [data])
utt.verify_grad(lambda v: argmin(v,axis=[1]), [data])
utt.verify_grad(lambda v: argmin(v, axis=[1]), [data])
utt.verify_grad(lambda v: argmin(v.flatten()), [data])
try:
grad(argmin(n,axis=-1),n)
grad(argmin(n, axis=-1), n)
raise Exception('Expected an error')
except TypeError:
pass
def test_grad_argmax(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
#test grad of argmax
utt.verify_grad(lambda v: argmax(v, axis=-1), [data])
utt.verify_grad(lambda v: argmax(v,axis=[0]), [data])
utt.verify_grad(lambda v: argmax(v, axis=[0]), [data])
utt.verify_grad(lambda v: argmax(v,axis=[1]), [data])
utt.verify_grad(lambda v: argmax(v, axis=[1]), [data])
utt.verify_grad(lambda v: argmax(v.flatten()), [data])
try:
grad(argmax(n, axis=-1),n)
grad(argmax(n, axis=-1), n)
raise Exception('Expected an error')
except TypeError:
pass
class T_min_max(unittest.TestCase):
def setUp(self):
utt.seed_rng()
MaxAndArgmax.debug = 0
def test0(self):
for fct in [max,min]:
def test_scalar(self):
for fct in [max, min]:
n = as_tensor_variable(5.0)
v = eval_outputs(fct(n))
self.assertTrue(v == 5.0)
v = eval_outputs(fct(n).shape)
assert len(v)==0
assert len(v) == 0
def test1(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]:
n = as_tensor_variable([1,2,3,2,-6])
def test_list(self):
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable([1, 2, 3, 2, -6])
v = eval_outputs([fct(n)])
self.assertTrue(v == nfct(n.value))
v = eval_outputs(fct(n).shape)
assert len(v)==0
assert len(v) == 0
def test2(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]:
data = numpy.random.rand(2,3)
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
v = eval_outputs(fct(n,-1))
self.assertTrue(numpy.all(v == nfct(data,-1)))
v = eval_outputs(fct(n, -1))
self.assertTrue(numpy.all(v == nfct(data, -1)))
v = eval_outputs(fct(n,-1).shape)
assert v==(2)
v = eval_outputs(fct(n, -1).shape)
assert v == (2)
def test2b(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]:
data = numpy.random.rand(2,3)
for fct, nfct in [(max, numpy.max),(min, numpy.min)]:
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
v = eval_outputs(fct(n,0))
self.assertTrue(numpy.all(v == nfct(data,0)))
v = eval_outputs(fct(n, 0))
self.assertTrue(numpy.all(v == nfct(data, 0)))
v = eval_outputs(fct(n,0).shape)
assert v==(3)
v = eval_outputs(fct(n,1).shape)
assert v==(2)
v = eval_outputs(fct(n,[0,1]).shape)
assert v.size==0
v = eval_outputs(fct(n, 0).shape)
assert v == (3)
v = eval_outputs(fct(n, 1).shape)
assert v == (2)
v = eval_outputs(fct(n, [0, 1]).shape)
assert v.size == 0
def test2_invalid(self):
for fct in [max,min]:
n = as_tensor_variable(numpy.random.rand(2,3))
for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
_logger.setLevel(logging.CRITICAL)
try:
try:
eval_outputs(fct(n,3))
eval_outputs(fct(n, 3))
assert False
except ValueError, e:
pass
finally:
_logger.setLevel(oldlevel)
def test2_invalid_neg(self):
for fct in [max,min]:
n = as_tensor_variable(numpy.random.rand(2,3))
for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
try:
eval_outputs(fct(n,-3))
eval_outputs(fct(n, -3))
assert False
except ValueError, e:
pass
finally:
sys.stderr = old_stderr
def test2_valid_neg(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2,3))
v = eval_outputs(fct(n,-1))
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
v = eval_outputs(fct(n, -1))
self.assertTrue(v.shape == (2,))
self.assertTrue(numpy.all(v == nfct(n.value,-1)))
v = eval_outputs(fct(n,-2))
self.assertTrue(numpy.all(v == nfct(n.value, -1)))
v = eval_outputs(fct(n, -2))
self.assertTrue(v.shape == (3,))
self.assertTrue(numpy.all(v == nfct(n.value,-2)))
self.assertTrue(numpy.all(v == nfct(n.value, -2)))
v = eval_outputs(fct(n,-1).shape)
assert v==(2)
v = eval_outputs(fct(n,-2).shape)
assert v==(3)
v = eval_outputs(fct(n, -1).shape)
assert v == (2)
v = eval_outputs(fct(n, -2).shape)
assert v == (3)
def test3(self):
for fct,nfct in [(max,numpy.max),(min,numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2,3,4))
v = eval_outputs(fct(n,0))
self.assertTrue(v.shape == (3,4))
self.assertTrue(numpy.all(v == nfct(n.value,0)))
v = eval_outputs(fct(n,1))
self.assertTrue(v.shape == (2,4))
self.assertTrue(numpy.all(v == nfct(n.value,1)))
v = eval_outputs(fct(n,2))
self.assertTrue(v.shape == (2,3))
self.assertTrue(numpy.all(v == nfct(n.value,2)))
v = eval_outputs(fct(n,[0,1]))
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2, 3, 4))
v = eval_outputs(fct(n, 0))
self.assertTrue(v.shape == (3, 4))
self.assertTrue(numpy.all(v == nfct(n.value, 0)))
v = eval_outputs(fct(n, 1))
self.assertTrue(v.shape == (2, 4))
self.assertTrue(numpy.all(v == nfct(n.value, 1)))
v = eval_outputs(fct(n, 2))
self.assertTrue(v.shape == (2, 3))
self.assertTrue(numpy.all(v == nfct(n.value, 2)))
v = eval_outputs(fct(n, [0, 1]))
self.assertTrue(v.shape == (4,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,1),0)))
v = eval_outputs(fct(n,[0,2]))
self.assertTrue(numpy.all(v == nfct(nfct(n.value, 1), 0)))
v = eval_outputs(fct(n, [0, 2]))
self.assertTrue(v.shape == (3,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,2),0)))
v = eval_outputs(fct(n,[1,2]))
self.assertTrue(numpy.all(v == nfct(nfct(n.value, 2), 0)))
v = eval_outputs(fct(n, [1, 2]))
self.assertTrue(v.shape == (2,))
self.assertTrue(numpy.all(v == nfct(nfct(n.value,2),1)))
v = eval_outputs(fct(n,[0,1,2]))
self.assertTrue(numpy.all(v == nfct(nfct(n.value, 2), 1)))
v = eval_outputs(fct(n, [0, 1, 2]))
self.assertTrue(v.shape == ())
v = eval_outputs(fct(n,0).shape)
assert tuple(v)==(3,4)
v = eval_outputs(fct(n,1).shape)
assert tuple(v)==(2,4)
v = eval_outputs(fct(n,2).shape)
assert tuple(v)==(2,3)
v = eval_outputs(fct(n,[0,1]).shape)
v = eval_outputs(fct(n, 0).shape)
assert tuple(v) == (3, 4)
v = eval_outputs(fct(n, 1).shape)
assert tuple(v) == (2, 4)
v = eval_outputs(fct(n, 2).shape)
assert tuple(v) == (2, 3)
v = eval_outputs(fct(n, [0, 1]).shape)
self.assertTrue(v == (4,))
v = eval_outputs(fct(n,[0,2]).shape)
v = eval_outputs(fct(n, [0, 2]).shape)
self.assertTrue(v == (3,))
v = eval_outputs(fct(n,[1,2]).shape)
v = eval_outputs(fct(n, [1, 2]).shape)
self.assertTrue(v == (2,))
v = eval_outputs(fct(n,[0,1,2]).shape)
v = eval_outputs(fct(n, [0, 1, 2]).shape)
self.assertTrue(v.size == 0)
def test_grad_max(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
def check_grad_max(data, max_grad_data, axis=None):
#This work only for axis in [0,None]
assert axis in [0,None]
assert axis in [0, None]
z = numpy.zeros_like(data)
z = z.flatten()
argmax=numpy.argmax(data,axis=axis)
if argmax.ndim==0:
z[numpy.argmax(data,axis=axis)]+=1
argmax = numpy.argmax(data, axis=axis)
if argmax.ndim == 0:
z[numpy.argmax(data, axis=axis)] += 1
else:
for id,v in enumerate(argmax):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
for id, v in enumerate(argmax):
z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape)
assert numpy.all(max_grad_data == z)
#test grad of max
#axis is the last one
utt.verify_grad(lambda v: max(v,axis=-1), [data])
utt.verify_grad(lambda v: max(v, axis=-1), [data])
utt.verify_grad(lambda v: max(v,axis=[0]), [data])
check_grad_max(data,eval_outputs(grad(max(n,axis=0).sum(),n)),axis=0)
utt.verify_grad(lambda v: max(v, axis=[0]), [data])
check_grad_max(data, eval_outputs(grad(max(n, axis=0).sum(), n)),
axis=0)
utt.verify_grad(lambda v: max(v,axis=[1]), [data])
utt.verify_grad(lambda v: max(v, axis=[1]), [data])
#check_grad_max(data,eval_outputs(grad(max(n,axis=1),n)),axis=1)
utt.verify_grad(lambda v: max(v.flatten()), [data])
check_grad_max(data,eval_outputs(grad(max(n.flatten()),n)))
check_grad_max(data, eval_outputs(grad(max(n.flatten()), n)))
def test_grad_min(self):
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
def check_grad_min(data, min_grad_data, axis=None):
#This work only for axis in [0,None]
assert axis in [0,None]
#This work only for axis in [0, None]
assert axis in [0, None]
z = numpy.zeros_like(data)
z = z.flatten()
argmin=numpy.argmin(data,axis=axis)
if argmin.ndim==0:
z[numpy.argmin(data,axis=axis)]+=1
argmin = numpy.argmin(data, axis=axis)
if argmin.ndim == 0:
z[numpy.argmin(data, axis=axis)] += 1
else:
for id,v in enumerate(argmin):
z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
for id, v in enumerate(argmin):
z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z = z.reshape(data.shape)
assert numpy.all(min_grad_data == z)
#test grad of min
#axis is the last one
utt.verify_grad(lambda v: min(v,axis=-1), [data])
utt.verify_grad(lambda v: min(v, axis=-1), [data])
utt.verify_grad(lambda v: min(v,axis=[0]), [data])
check_grad_min(data,eval_outputs(grad(min(n,axis=0).sum(),n)),axis=0)
utt.verify_grad(lambda v: min(v, axis=[0]), [data])
check_grad_min(data, eval_outputs(grad(min(n, axis=0).sum(), n)),
axis=0)
utt.verify_grad(lambda v: min(v,axis=[1]), [data])
utt.verify_grad(lambda v: min(v, axis=[1]), [data])
#check_grad_min(data,eval_outputs(grad(min(n,axis=1),n)),axis=1)
utt.verify_grad(lambda v: min(v.flatten()), [data])
check_grad_min(data,eval_outputs(grad(min(n.flatten()),n)))
check_grad_min(data, eval_outputs(grad(min(n.flatten()), n)))
def _grad_list(self):
"""
Test the gradient when we have multiple axis at the same time.
This not implemented, so we disable the test. See ticket: http://trac-hg.assembla.com/theano/ticket/511
This not implemented, so we disable the test. See ticket:
http://trac-hg.assembla.com/theano/ticket/511
"""
data = numpy.random.rand(2,3)
data = numpy.random.rand(2, 3)
n = as_tensor_variable(data)
for fct in [max_and_argmax,max,min]:
utt.verify_grad(lambda v: fct(v,axis=[0,1]), [data])
#check_grad_max(data,eval_outputs(grad(max_and_argmax(n,axis=1)[0],n)),axis=1)
for fct in [max_and_argmax, max, min]:
utt.verify_grad(lambda v: fct(v, axis=[0, 1]), [data])
#check_grad_max(data, eval_outputs(grad(max_and_argmax(n,
#axis=1)[0], n)),axis=1)
class T_subtensor(unittest.TestCase):
"""
This is build in a way that allow to reuse it to test the equivalent gpu op.
This is build in a way that allow to reuse it to test the
equivalent gpu op.
"""
def __init__(self, name, shared=_shared,
sub=tensor.Subtensor,
......
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